Requirement: Quality tests are often carried out on the basis of statistical specifications. If a defect is discovered in the process, further tests must be carried out. Moreover, since testing is usually only carried out on a random basis, there is always a residual risk that defective workpieces will be used. To minimize these risks, it makes sense to inspect every workpiece while it is being produced or shortly after it has been produced - and to do so directly on the machine.

Solution approach: This is not done by elaborate sensor technology or measuring instruments but by observing the data available in the CNC. Bad parts are identified immediately. An app based on a mathematical calculation algorithm, which has been trained with data sets of good and bad parts, takes over this task during production/at runtime. The special feature is that not only individual signals are considered and monitored in isolation but several signals simultaneously and even sum signals.

Our solution: The Analyze MyWorkpiece /Monitor edge app allows you to configure the signals to be monitored, train the computational algorithm, and monitor the processing data at runtime. Analyze MyWorkpiece /Monitor can thus significantly reduce the effort required for manual testing.

Requirement: Even in optimized and thoroughly automated processes, errors can occur when inserting workpieces into the machine tool. Reasons for this can be, for example: crooked clamping due to chip residues in the clamping device or pressure drops in pneumatic clamping devices or incorrect positioning of workpieces. This leads not only to increased scrap and costs due to unnecessary machine hours, but also to increased tool wear or even breakage.

Solution approach: This can be avoided if errors are prevented as early as the insertion/clamping of the workpiece. A system consisting of a camera, app and artificial intelligence (AI) helps to reliably insert workpieces correctly in order to ensure the quality of the machining process. 

Our solution: The Protect MyMachine /Setup edge app is used to upload reference images to the cloud, where they are used to train the monitoring algorithm. The trained algorithm is then transferred back to Protect MyMachine /Setup and from that point on the app takes over the monitoring of the images of the clamping situation captured by the camera.

Tip: The quality of the finished workpiece can also be checked with this system.

Requirement: Tool wear cannot be precisely predicted due to uncertainties in the process such as fluctuating stock allowances or different raw material qualities. In the worst case, it can lead to poor part quality or even tool breakage.

Solution approach: It makes sense to check the condition of the tool cutting edge regularly - e.g. during tool change - in order to switch to a new tool in good time. In this way, the process remains stable, the desired component quality is achieved, or tools are only replaced when they are actually worn out. A system consisting of a microscopic camera, app and artificial intelligence helps to monitor the wear of the tool cutting edge on the machine.

Our solution: With the Analyze MyWorkpiece /ToolCheck edge app, reference images are loaded into the cloud to train the algorithm for monitoring. The trained algorithm is then transferred back to Analyze MyWorkpiece /ToolCheck. From this point on, the app takes over the monitoring of the images of the tool cutting edge captured by the camera.

Requirement: Single-part production faces special requirements, especially when dealing with large, expensive workpieces or expensive materials such as titanium. Here, you cannot afford to spend a long time optimizing on the machine, carrying out numerous tests with workpieces or even having to laboriously rework them. First-time-right is the goal!

Solution approach: Companies usually receive a CAD file from the customer and create their CNC program from it. Optimization of this program ideally takes place before machining starts. In this phase, errors can be eliminated on the one hand and optimizations such as non-productive time reduction can be made on the other, without requiring material or tools.

Our solution: The PC software Analyze MyWorkpiece /Toolpath with its 3D surface reconstruction makes it possible to visually detect and analyze defects in work preparation. For this purpose, the data recorded by the Edge App Analyze MyWorkpiece /Capture is imported and displayed as a 3D model. In this way, employees in work preparation are able to quickly and easily identify opportunities for optimization or troubleshooting.

Tip: Non-productive time reduction is also an important factor for series production. In this way, new series can be optimized and existing clocked machining operations can be further optimized.